Better understanding of customer needs, way of working and operational context will enable focus in R&D investment.

Early market introduction leads to better competitive positioning. Note: only top 3 players have success, however only true when the market introductions meet adequate quality criteria.

Industrial costs of poor quality sometimes amount to up to 5% of total turnover, typically more than 20% of the R&D budget is spent on resolving poor quality related problems.

There is a strong industrial need to employ scarce human resources to the R&D activities that generate the most business impact.

Access to the OEMs development processes directly translate in new primary business capabilities, for example by creating opportunities to develop new tool functionality and services for an as of yet underexplored area of application.

Possibility to incubate and validate proof of concepts for (general purpose) tool functionality and services in industrial context.

Methodology

ESI develops generic methodologies that integrate domain knowledge and data into a system-level reasoning framework.”. As shown in the above figure, for knowledge engineering, ESI uses domain-specific languages to systematically model domain knowledge, which is often scattered across documents, or in the heads of experts. For data-driven business applications, knowledge engineering supports the effective analysis of operational data, by applying, for example, feature selection or constraints in learning algorithms. Exploitation of profiling, process and data mining techniques allow the generation of context-specific operational models that can either support, among others, automated testing or customization of system operations or be used to develop new systems.

Lessons learned

Reflexion project figures out the main challenges are how to valorize the emerging operational data in high-tech systems, how to react to emerging needs in an efficient and cost-effective way by augmenting products with an introspective layer of data sensinganddata analytics? The learned lessons include

The high-tech industry (R&D) still has an immature view on the exploitation of data science for valorization of operational data. Building up awareness, that integration of data into smart engineering processes can become a valuable and competitive business asset, currently turns out to be more important than building up expertise.

Focus should be bridging the world of data science with the world of high-tech systems development, by exploiting data analytics expertise / techniques to extract value out of existing operational system logging.

High-tech systems are quite diverse: there are little to no out-of-the-box generic (data science) approaches, in reality it requires serious domain insight / modeling mixed with real system understanding craftmanship.

Developed and released a data analysis framework which collects & visualizes machine data of the fleet of operational MRI systems, supporting services incl. MRI system dashboards for commercial customers.

Developed and validated a methodology to train machine learning algorithms with simulation-generated data for conditional monitoring of machines.

Bram Cornelis

all partners

25 data science specialists (FTEs) were employed by the consortium partners to work on operational data roadmap activities as a direct result of the project activities.

Bas Huijbrechts

Axini, SynerScope, Yazzoom

10 commercial tool set releases including - in the project developed - generic purpose data visualization, analytics and MBT functionality to be applied in an industrial setting directly resulted from the Reflexion project activities.

Bas Huijbrechts

“My drive is to scale up knowledge valorisation, balancing research objectives against the industrial expectations, for architecting and designing complex systems into the high-tech embedded industry for real industry valorisation.”